compute.threshold.AROC.kernel {AROC} | R Documentation |

## AROC-based threshold values.

### Description

Estimates AROC-based threshold values using the nonparametric kernel-based method proposed by Rodriguez-Alvarez et al. (2011).

### Usage

```
compute.threshold.AROC.kernel(object, newcovariate, FPF = 0.5)
```

### Arguments

`object` |
An object of class |

`newcovariate` |
Numeric vector with the covariate values at which threshold values are required. |

`FPF` |
Numeric vector with the FPF at which to calculate the AROC-based threshold values. Atomic values are also valid. |

### Details

Estimation of the covariate-adjusted ROC curve (AROC) using the nonparametric kernel-based method proposed by Rodriguez-Alvarez et al. (2011) involves the estimation of the conditional distribution function for the diagnostic test outcome in the healthy population

`F_{\bar{D}}(y|X_{\bar{D}}) = Pr\{Y_{\bar{D}} \leq y | X_{\bar{D}}\}.`

This function makes use of this estimate in order to calculate AROC-based threshold values. In particular, for a covariate value `x`

and a FPF = t, the AROC-based threshold value is calculated as follows

`\hat{c}_{\mathbf{x}} = \hat{F}^{-1}_{\bar{D}}(1-t|\mathbf{X}_{\bar{D}} = \mathbf{x}).`

### Value

A matrix with the computed AROC-based threshold values. The matrix has as many columns as different covariate values, and as many rows as different FPFs.

### References

Hayfield, T., and Racine, J. S.(2008). Nonparametric Econometrics: The np Package. Journal of Statistical Software 27(5). URL http://www.jstatsoft.org/v27/i05/.

Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.

Rodriguez-Alvarez, M. X., Roca-Pardinas, J., and Cadarso-Suarez, C. (2011). ROC curve and covariates: extending induced methodology to the non-parametric framework. Statistics and Computing, 21(4), 483 - 499.

### See Also

### Examples

```
library(AROC)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]
# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
m2 <- AROC.kernel(marker = "l_marker1", covariate = "age",
group = "status", tag.healthy = 0, data = newpsa,
p = seq(0,1,l=101), B = 500)
# Compute the threshold values
cov.values <- seq(52, 80, l = 50)
FPF = c(0.1, 0.3)
th_np <- compute.threshold.AROC.kernel(m2, cov.values, FPF)
```

*AROC*version 1.0-4 Index]